64 lines
2.0 KiB
C++
64 lines
2.0 KiB
C++
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// Copyright (c) OpenMMLab. All rights reserved.
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#include "codebase/mmocr/panet.h"
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#include "opencv2/imgproc.hpp"
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namespace mmdeploy::mmocr {
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class PaHeadCpuImpl : public PaHeadImpl {
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public:
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Result<void> Process(Tensor text_pred, //
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Tensor kernel_pred, //
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Tensor embed_pred, //
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float min_text_confidence, //
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float min_kernel_confidence, //
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cv::Mat_<float>& text_score, //
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cv::Mat_<uint8_t>& text, //
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cv::Mat_<uint8_t>& kernel, //
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cv::Mat_<int>& label, //
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cv::Mat_<float>& embed, //
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int& region_num) override {
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OUTCOME_TRY(stream_.Wait());
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auto height = static_cast<int>(text_pred.shape(1));
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auto width = static_cast<int>(text_pred.shape(2));
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text_score = cv::Mat_<float>(height, width, text_pred.data<float>());
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sigmoid(text_score);
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text = text_score > min_text_confidence;
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cv::Mat_<float> kernel_score(height, width, kernel_pred.data<float>());
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sigmoid(kernel_score);
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kernel = kernel_score > min_kernel_confidence & text;
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auto n_embed_channels = static_cast<int>(embed_pred.shape(0));
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embed = cv::Mat_<float>(n_embed_channels, height * width, embed_pred.data<float>());
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cv::transpose(embed, embed);
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region_num = cv::connectedComponents(kernel, label, 4, CV_32S);
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return success();
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}
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static void sigmoid(cv::Mat_<float>& score) {
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cv::exp(-score, score);
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score = 1 / (1 + score);
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}
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};
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class PaHeadCpuImplCreator : public ::mmdeploy::Creator<PaHeadImpl> {
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public:
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const char* GetName() const override { return "cpu"; }
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int GetVersion() const override { return 0; }
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std::unique_ptr<PaHeadImpl> Create(const Value&) override {
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return std::make_unique<PaHeadCpuImpl>();
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}
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};
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REGISTER_MODULE(PaHeadImpl, PaHeadCpuImplCreator);
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} // namespace mmdeploy::mmocr
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